A Novel Prognostic Ferroptosis-Related Long Noncoding RNA Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common primary malignancy of renal cancer in adults. Ferroptosis is critically associated with the prognosis of ccRCC. However, knowledge of long noncoding RNA- (lncRNA-) related ferroptosis that affects the prognosis of ccRCC is still insufficient. Using the LASSO regression, we created a risk model based on differentially expressed ferroptosis-related lncRNAs (FRLRS) in ccRCC. The analysis of Kaplan–Meier for survival, area under the curve (AUC) for diagnosis, nomogram for predicting overall survival, and gene expression for immune checkpoints were performed based on the screened independent prognostic factors. Nine lncRNAs were found to be associated with ccRCC prognosis. Furthermore, the prognostic AUC of the FRLRS signature was 0.78, demonstrating its usefulness in predicting ccRCC prognosis. The lncRNA risk model outperformed the standard clinical variables in predicting ccRCC prognosis. Finally, The Cancer Genome Atlas revealed that T cell functions, such as cytolytic activity, human leukocyte antigen activity, inflammation regulation, and type II interferon response coordination, are significantly different between two different risk levels of ccRCC. Immune checkpoints were also expressed differently in programmed cell death 1 receptor, inducible T cell costimulator, cytotoxic T-lymphocyte antigen-4, and leukocyte-associated immunoglobulin-like receptor 1. The nine FRLRS signature models may affect the prognosis of ccRCC.


Introduction
By far the most common type, renal cell carcinoma (RCC) is thought to originate in the renal epithelium in the kidney and affects over 400,000 individuals worldwide annually [1,2]. Previous studies have reported that ccRCC is characterized as a highly metabolic disease, and fetal tumors are likely to be fundamental to the development of renal cancerrelated deaths [3].
Currently, localized ccRCC can be treated with partial or radical nephrectomy [4], ablation [5], or active surveillance [6]. Approximately 30% of ccRCC who recur with the localized disease eventually develop metastases following curative nephrectomy [7][8][9], which be associated with higher mortality and requires systemic therapy. Mammalian targets of the rapamycin pathway have been developed. However, the response to treatment varies, and most ccRCC patients eventually make progression [10]. Treatment seems to have had a positive effect on the prognosis of patients based on the expression of clinical diagnostic markers. erefore, identifying biomarkers is essential for effective and rapid intervention and treatment of ccRCC patients [11,12].
Recently, a novel form of cell death, termed ferroptosis, was first proposed by Dixon in 2012 [13]. Ferroptosis is a form of unique iron-reliant and reactive oxygen autophagydependent on cell death with characteristics of cytological changes, such as diminished or decreased mitochondrial cristae and mitochondrial membrane condensed [14][15][16][17].
Mounting evidence indicates that ferroptosis is involved in diverse physiological and pathological conditions in human disease [18]. Hepatocellular carcinoma [19], gastric cancer [20,21], and other cancers [22,23] have been proven to be ferroptosis-related. erefore, these include targeting ferroptosis has been suggested in cancer therapeutic [17]. Biologically, kidney disease is a metabolic disorder and is associated with the iron metabolism [24]. Recent studies demonstrated that ferroptosis is associated with ccRCC [25,26]. Long noncoding RNAs (lncRNAs) have a critical predictive value in various cancers' occurrence, progression, and prognosis [27][28][29]. Wu conducted a novel ferroptosisassociated genes model based on the clinical significance in predicting pancreatic cancer [30]. An increasing number of genes related to ferroptosis have been found. However, the association between ferroptosis-related lncRNAs (FRLRS), and their prognostic value in ccRCC is yet to be understood.
Here, we provide new insights to assess the prognostic value of FRLRS in ccRCC. Using bioinformatics analysis, we established independent prognostic multiple FRLRS signatures and estimated lncRNAs in the immunotherapy response by inhibitory concentration.

Data
Collection. RNA sequences of ccRCC patients' data were downloaded from TCGA-KIRC (72 patients were normal, 539 patients had tumors). Taking the corresponding genes related to ferroptosis in FerrDb [31] provides the most comprehensive database of iron bacteria and related disease markers by a web-based alliance. Our study identified 259 ferroptosis-related genes ( Figure 1, Supplementary  Table S1). If the correlation |R| was >0.5 at p < 0.05, the association between FRLRS and ccRCC was considered significant.
e clinical information data collected from patients with ccRCC included gender, age, stage, grade, tumor-node-metastasis (TNM), survival status, and followup time.
e additional biological function of the differentially expressed FRLRS was analyzed based on GO and KEGG data using R software via the package "cluster profile," "ggplot2," and "enrichplot."

Construction of the FRLRS Prognostic Model.
A machine learning method (LASSO) was used to identify hub genes more efficiently. Univariate Cox analysis combined with multivariate Cox regression analysis identified significant increments associated with FRLRS. LASSO was utilized to construct the FRLRS signature. e equation of risk score � (β1 × FRLRS − 1) + (β2 × FRLRS − 2) +· · ·+ (βn × FRLRS − n). We established the hybrid nomogram using the selected FRLRS prognostic signature and independent factors in TCGA-KIRC. Based on the median expression levels of FRLRS, ccRCC patients were divided into different risk groups (high-risk and low-risk groups). According to the clinical variables and FRLRS in the hybrid nomogram, the ROC analysis was performed to estimate the accuracy of 1-year, 3-year, and 5-year over survival (OS).

Molecular Mechanism and Immune Infiltration Enrichment Analyses.
Six enrichment analyses algorithms, including the CIBERSORT [32,33], ESTIMATE, MCPconuter [34], ssGSEA [35], TIMER [36], and xCell algorithms, were compared to evaluate enrichment scores and cellular components in the two risk levels groups according to screen lncRNA signature. Enrichment analyses algorithms were applied to definite enrichment scores representing the gene set absolute enrichment in each sample with the "GSVA" package. Potential immune checkpoints were retrieved from the published literature [37]. Moreover, the TIDE model, trained from treatment-naive tumor data, can predict the likelihood of the immunotherapeutic response [38][39][40].

Statistical
Analysis. LASSO, Cox analysis, and heatmaps were used to assess the correlation between FRLRS and clinicopathological characteristics. e therapeutic response was estimated by the TIDE model and the half-max inhibitory concentration (IC 50) obtained from the GDSC website. Kaplan-Meier survival curves analysis and principal component analysis (PCA) evaluated patients with ccRCC based on the FRLRS signature. Our analyses were performed in the R statistical (4.0.2).

Results
According to TCGA and FerrDb data, 76 ferroptosis-related DEGs were identified (42 upregulated and 34 downregulated genes) (Supplementary Table S2). BP is involved in cell production in response to chemical stress, hypoxia, cofactor metabolism, and oxidative stress. MF mainly regulates iron ion binding, coenzyme binding, and oxidoreductase activity. It acts on a single donor by combining molecular oxygen and nicotinamide adenine dinucleotide phosphate oxidase. CC was mainly elevated in the apical part of the cell, apical plasma membrane, and basolateral plasma membrane. KEGG analysis shows overexpressed genes were mainly involved in HIF-1, microRNA in cancer, Kaposi sarcomaassociated herpesvirus infection, arachidonic acid metabolism, proteoglycan in cancer, human giant cell virus infection, ferroptosis, biosynthesis of amino acids, fluid shear stress and arteriosclerosis, autophagy-animal, and serotonergic synapse (Figure 2(a) and Supplementary Table S3).
e waterfall plot displays mutation information of the 76 ferroptosis-related DEGs in TCGA-KIRC entire set ( Figure 2(b)) and in two risk groups (Figures 2(c) and 2(d)).

FRLRS Set Analyses and Construction Hybrid Nomogram.
GO analysis and GSEA for the biological function of these FRLRS are shown in Figure 5. e novel FRLRS prognostic signature regulated could be found in tumor and immune-related pathways, including cytokine-receptor interaction, glycerophospholipid metabolism, homologous recombination, IgA production, primary immunodeficiency, endometrial cancer, peroxisome, propanoate metabolism, prostate cancer, valine leucine, and isoleucine degradation (Supplementary Table S6). As shown in Figure 6(a), we found a distinct distribution pattern between two different groups of ccRCC patients in regard to prognosis. Figure 6(b) shows the survival status and time of ccRCC patients in two different risk levels of ccRCC patients. Figure 6(c) shows the relative expression of 9 FRLRS for each ccRCC patient. Moreover, the prognosis AUC of FRLRS signature was 0.78, outperforming traditional clinical characteristics in predicting ccRCC patients ( Figure 6(d)). e DCA of the risk level and other clinicopathological features shows that the prognostic risk model of 9 FRLRS for ccRCC was comparatively dependable (Figure 6(e)). e concordance index (C-index) shows that the risk model performs better than  (Figure 7(b)). e calibration plot of the nomogram is shown in Figure 7(c).

Principal Component Analysis (PCA) and Survival
Analysis.

Discussion
We first identified FRLRS signatures using a combined analysis of TCGA-KIRC and FerrDb datasets in this study. is novel approach may lead to new immunotherapeutic targets for tumor treatment. During the development of immunotherapy, we explored whether FRLRS are correlated with immune cells and immunotherapy response in ccRCC prognosis.
ese findings led to the identification of potential biomarkers or immunotherapy targets in ferroptosis signaling pathways. According to TCGA and FerrDb data, 76 ferroptosis-related DEGs were identified. We obtained 1502 FRLRS in TCGA-KIRC. Combined with LASSO Cox analysis, 106 FRLRS were identified. KEGG analysis revealed that the genes mainly involved in Kaposi sarcoma-associated herpesvirus infection, miRNAs in cancer, arachidonic acid metabolism, proteoglycans in cancer, human cytomegalovirus infection, ferroptosis, biosynthesis of amino acids, fluid shear stress and atherosclerosis, autophagy-animal, and serotonergic synapse. Furthermore, the in-depth analysis revealed 9 differentially expressed lncRNAs (AC026401.3, LINC01615, PRKAR1B-AS1, LINC02609, LINC00460, AC084876.1, AC008870.2, LINC02747, and AC103706.1). PCA shows that the model using FRLRS can distinguish well between ccRCC patients with two different risk levels. e K-M curve shows that ccRCC patients in the low-risk group have a better survival prognosis. ese FRLRS may be independent prognostic biomarkers for ccRCC patients.   Among these lncRNAs, LINC01615 was identified as a metastasis-related lncRNA in HCC [41]. LINC00460 was also found to be critical for multiple tumorigeneses. A recent study found that LINC00460 could promote epithelialmesenchymal transition in HNSCC by facilitating peroxiredoxin-1 [42]. A related study suggested that LINC00460 is a potential biomarker related to outcomes in malignant tumors [43], which provides critical insights into the targeting of FRLRS in predicting ccRCC. Recently, high LINC02747 expression was significantly associated with advanced tumor TNM stage, histological grade, and poor outcome, thus promoting the proliferation of ccRCC   according to inhibit miR-608 [44]. Collectively, these findings provide an essential basis for this study regarding the association between lncRNAs and ccRCC. Nevertheless, our findings may provide a research direction on the role of FRLRS in ccRCC prognosis for cancer treatment. e ssGSEA algorithm revealed that T cell functions, such as APC costimulation and CCR checkpoint, and cytolytic activities, promoting inflammation and parainflammation, were significantly different in different risk groups of ccRCC. Combination ferroptosis with ICIs can synergistically promote antitumor activity, even in ICI resistance [45]. Because of the importance of immunotherapy based on checkpoint inhibitors, our data revealed a substantial difference expression with immune checkpoint-related genes in both groups of ccRCC patients, highlighting the potential significance of FRLRS in regulating ICIs. e total number of somatic encoding mutation (TMB) is a potent biomarker for predicting the response to immunotherapy. As in other studies [40], TMB exceeded the highrisk ccRCC group. Furthermore, several studies have validated that TIDE algorithms can serve as a predictive model for immunotherapy [38][39][40]. In this study, ccRCC patients in the low-risk group have better immunotherapy response. Ten candidate compounds for KIRC differentiation were identified in our study.
us, considering that clinical samples do not verify the results of this study, there is no guarantee that reliability can be directly tested.

Conclusion
In summary, we elucidated that specific FRLRS in the prognostic prediction of ccRCC. Furthermore, our current findings may provide more valuable insights for future ccRCC research by much more large clinical trials.

Data Availability
e datasets used to support the findings of this study are publicly available in the TIDE database, FerrDb, and TCGA database (https://portal.gdc.cancer.gov/).

Conflicts of Interest
e authors declare that they have no conflicts of interest.